Data Validation Is a Critical Step in The CRM Process: Here’s What to Consider


Data validation is a critical step in your Constituent Relationship Management (CRM) implementation project. For the best results, you must get out of thinking that anything having to do with data conversion is a technical task or an activity that only requires the involvement of the technical team members.


Instead, consider using your Subject Matter Experts (SMEs). Using your SMEs in the advancement CRM implementation project adds value because they live and breathe the data daily. Of course, there are technical components and things the technical team will do, but for the best results, you must combine that with your SMEs.


Here’s how you can get your SMEs involved in the data validation process.


Step One: Establish a Framework

Establish the framework for how to involve regular business users in the process. Ensure the data validation compares what’s in your legacy system and your new advancement CRM.


  • Is the data there?
  • Did it land in the right place in your new advancement CRM?


As you evaluate the data accuracy, consider things like proper orientations, instructions, and available preparations for navigating to where the data lives in the legacy system.


Specifically, helping your business users to navigate the data’s “new home” in the advancement CRM. Navigation is vital because if you compare data and involve the business users, they must know how to navigate. Otherwise, they’re at a loss.


Step Two: Create a Validation Checklist

Involving your business users in the data validation process requires clear instructions on how they can (and should) participate. In many cases, decisions may have either changed it or left data behind when it moved from the old system to the new one.


From the business users’ perspective, they’re working within the system daily and may find themselves exploring beyond the project outline. While their input is valuable, you don’t want them to get lost in their exploration. To help deter this, create a validation checklist that outlines the minimum requirements they should perform that will be critical in giving feedback to your CRM project.


For example, create a list of 15-20 items of minimum requirements that must be met from an assignment perspective. Once those requirements are completed, then you can encourage further exploration. This helps your CRM project stay on track without losing sight of priority validation items that need to be completed.


The data validation checklist is one way to balance tasks getting done to move the project forward versus encouraging curiosity during a project.


Step Three: Determine the Records to Test

There are pockets of populations within your advancement CRM that have more data or are more prominent individuals and organizations that you need to pay attention to as you complete your data validation.


For example, if something went wrong with your VIP list in the legacy system—that would be very noticeable—and should be a part of the group of records you will test in the new advancement CRM.


Deciding what records are the most valuable to test requires prioritization. Consider these lists as you plan what to prioritize.


  • VIPs
  • Board members
  • Trustees
  • Major donors
  • Organizational partnerships
  • Foundations


Be thoughtful about building the test record population, as this is a necessary step toward effective data validation.


Step Four: Generate Guidelines for Reports

The final step toward involving your SMEs in the data validation process is to generate a particular framework on how they can report results. Your SMEs are a population of users that don’t do this regularly—these aren’t your technical team members who understand the testing methodology. Therefore, you must provide an easy-to-follow framework and instructions for how to report results, so you get meaningful information.


Having clear reporting strategies also helps to eliminate the back-and-forth communication or vague generalizations like, “This didn’t work.” Instead, generate guidelines for how users can report challenges. For example, include prompts to help identify specific issues and an explanation of the problem, “Which test record were you on and what happened?”


Encourage screenshots and videos so they can share what they experienced most efficiently.


Data validation provides accuracy, clearness, and completeness to your new advancement CRM dataset—avoiding errors and ensuring data is not corrupted. While data validation can be performed by your technical team, involving your SMEs adds significant value and enhances your CRM project.